Integrated Systems of Meso-Meteorological and Chemical Transport Models docx

Integrated Systems of Meso-Meteorological
and Chemical Transport Models
.
Alexander Baklanov
l
Alexander Mahura
l
Ranjeet S. Sokhi
Integrated Systems of
Meso-Meteorological
and Chemical Transport
Models
Editors
Prof. Alexander Baklanov
Danish Meteorological Institute
Lyngbyvej 100
DK-2100 Copenhagen
Denmark

Dr. Alexander Mahura
Danish Meteorological Institute
Lyngbyvej 100
DK-2100 Copenhagen
Denmark

Prof. Ranjeet S. Sokhi
University of Hertfordshire
College Lane
Ctr. Atmospheric & Instrumentation
Research (CAIR)

AL10 9AB Hatfield
United Kingdom

ISBN 978-3-642-13979-6 e-ISBN 978-3-642-13980-2
DOI 10.1007/978-3-642-13980-2
Springer Heidelberg Dordrecht London New York
# Springer-Verlag Berlin Heidelberg 2011
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Preface
Weather natural hazards, the environment and climate change are of concern to all of
us. Especially, it is essential to understand how human activities might impact the
nature. Hence, monitoring, research, and forecasting is of the outmost importance.
Furthermore, climate change and pollution of the environment do not obey national
borders; so, international collaboration on these issues is indeed extremely important.
In the future, the increasing computer power and understanding of physical
processes pave the way for developing integrated models of the Earth system and
gives a possibility to include interactions between atmosphere, environment,
climate, ocean, cryosphere and ecosystems.
Therefore, development of integrated Numerical Weather Prediction (NWP) and

Atmospheric Chemical Transport (ACT) models is an important step in this strate-
gic direction and it is a promising way for future atmospheric simulation systems
leading to a new generation of models. The EC COST Action 728 “Enhancing
Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion
Applications” (2004–2009) is aimed at identifying the requirements and propose
recommendations for the European strategy for integrated mesoscale NWP-ACT
modelling capability.
DMI strongly supports this development. Almost 10 years ago DMI initiated
developing an on-line integrated NWP-ACT modelling system, now called Enviro-
HIRLAM (Environment – HIgh Resolution Limited Area Model), which includes
two-way interactions between meteorology and air pollution for NWP applications
and chemical weather forecasting. Recently we also initiated organisation of the
Chemical branch in the HIRLAM international consortium (),
where this model is considered as the baseline model. The Enviro-HIRLAM
became an international community model starting January 2009 with several
external European organisations joining the research and development team (e.g.,
from the University of Copenhagen, Denmark; University of Tartu, Estonia;
University of Vilnius, Lithuania; Russian State Hydro-Meteorological University;
Tomsk State University, Russia; Odessa State Environmental University, Ukraine)
with new coming participants.
During 2002–2005, DMI led EC FP5 project FUMAPEX (.
dk), which developed a new generation Integrated Urban Air Quality Information
and Forecasting System and implemented such a system in six European cities.
v
The new EC FP7 project MEGAPOLI (2008–2011) (o), coordi-
nated by DMI, is also focusing on further developments of integrated systems and
studies of interactions between atmospheric pollution from mega cities and meteo-
rological and climatic processe s.
These remarks show the importance to organise a workshop to share and analyse
international experience in integrated modelling worldwide. The first workshop on

“Integration of meteorological and chemical transport models” (.
fi/Integ07) was arranged at DMI (Copenhagen, Denmark) on 21–23 May 2007. The
workshop was organised in the framework of the COST Action 728 and in cooper-
ation with the Nordic Network on Fine-scale Atmospheric Modelling. Almost 50
participants, including invited experts in integrated modelling and young scientists,
from 20 countries attended this event to discuss the experience and further per-
spectives of coupling air quality and meteorology in fine-scale models. The work-
shop was aimed at joining both NWP and air quality modellers to discuss and make
recommendations on the best practice and strategy for further developm ents and
applications of integrated and coupled modelling systems “NWP and Meso-Meteo-
rology – Atmospheric Chemical Transport”. Main emphasis was on fine-resolution
models applied for local chemical weather forecas ting and considering feedback
mechanisms between meteorological and atmospheric pollution (e.g. aerosols)
processes. The following topics were in the focus of presentations and discussions:
l
Online and offline coupling of meteorological and air quality models
l
Implementation of feedback mechanisms, direct and indirect effects of aerosols
l
Advanced interfaces between NWP and ACT models
l
Model validation studies, including air quality-related episode cases
As a follow-up a young sci entist summer school and workshop on “Integrated
Modelling of Meteorological and Chemical Transport Processes / Impact of Chem-
ical Weather on Numerical Weather Prediction and Climate Modelling” was
organised by DMI and Russian State Hydrometeorological University during
7–15 July 2008 in Russia.
This book, written mostly by invited lectors/speakers of the Copenhagen work-
shop, is focused on above mentioned workshop topics, summarizes presentations,
discussions, conclusions, and provides recommendations. The book is one of the first

attempts to give an overall look on such integrated modelling approach. It reviews the
current situation with the on-line and off-line coupling of mesoscale meteorological
and air quality models around the world (in European countries, USA, Canada, Japan,
Australia, etc.) as well as discusses advantages and disadvantages, best practice, and
gives recommendations for on-line and off-line coupling of NWP and ACT models,
implementation strategy for different feedback mechanisms, direct and indirect
effects of aerosols and advanced interfaces between both types of models.
It is my hope that this book will be useful for first of all to those interested in the
modelling of meteorology and air pollution, but also for the entire meteorology and
atmospheric environment communities, including students, researchers and practi-
cal users.
Copenhagen, Denmark DMI Director General, Peter Aakjær
vi Preface
Contents
1 Introduction – Integrated Systems: On-line and Off-line Coupling
of Meteorological and Air Quality Models, Advantages and
Disadvantages 1
Alexander Baklanov
Part I On-Line Modelling and Feedbacks
2 On-Line Coupled Meteorology and Chemistry Models
in the US 15
Yang Zhang
3 On-Line Chemistry Within WRF: Description and
Evaluation of a State-of-the-Art Multiscale Air Quality
and Weather Prediction Model 41
Georg Grell, Jerome Fast, William I. Gustafson Jr, Steven E. Peckham,
Stuart McKeen, Marc Salzma nn, and Saulo Freitas
4 Multiscale Atmospheri c Chemistry Modelling with GEMAQ 55
Jacek Kaminski, Lori Neary, Joanna Struzewska,
and John C. McConnell

5 Status and Evaluation of Enviro-HIRLAM: Differences
Between Online and Offline Models 61
Ulrik Korsholm, Alexander Baklanov, and Jens Havskov Sørensen
6 COSMO-ART: Aerosols and Reactive Trace Gases Within
the COSMO Model 75
Heike Vogel, D. Ba
¨
umer, M. Bangert, K. Lundgren, R. Rinke,
and T. Stanelle
vii
7 The On-Line Coupled Mesoscale Climate–Chemistry Model
MCCM: A Modelling Tool for Short Episodes as well as
for Climate Periods 81
Peter Suppan, R. Forkel, and E. Haas
8 BOLCHEM: An Integrate d System for Atmospheric
Dynamics and Composition 89
Alberto Maurizi, Massimo D’Isidoro, and Mihaela Mircea
Part II Off-Line Modelling and Interfaces
9 Off-Line Model Integration: EU Practices, Interfaces,
Possible Strategies for Harmonisation 97
Sandro Finardi, Alessio D’Allu ra, and Barbara Fay
10 Coupling Global Atmospheric Chemistry Transport Models
to ECMWF Integrated Forecasts System for Forecast
and Data Assimilation Within GEMS 109
Johannes Flemming, A. Dethof, P. Moinat, C. Ordo
´
n
˜
ez,
V H. Peuch, A. Segers, M. Schultz, O. Stein, and M. van Weele

11 The PRISM Support Initiative, COSMOS and OASIS4 125
Rene
´
Redler, Sophie Valcke, and Helmuth Haak
12 Integrated Modelling Systems in Australia 139
Peter Manins, M.E. Cope, P.J. Hurley, S.H. Lee, W. Lilley,
A.K. Luhar, J.L. McGregor, J.A. Noonan, and W.L. Physick
13 Coupling of Air Quality and Weather Forecasting:
Progress and Plans at met.no 147
Viel Ødegaard, Leonor Tarraso
´
n, and Jerzy Bartnicki
14 A Note on Using the Non-hydrostatic Model AROME
as a Driver for the MATCH Model 155
Lennart Robertson and Valentin Fo ltescu
15 Aerosol Species in the Air Quality Forecasting System of FMI:
Possibilities for Coupling with NWP Models 159
Mikhail Sofiev and SILAM Team
16 Overview of DMI ACT-NWP Modelling Systems 167
Alexander Baklanov, Alexander Mahura, Ulrik Korsholm,
Roman Nuterman, Jens Havskov Sørensen, and Bjarne Amstrup
viii Contents
Part III Validation and Case Studies
17 Chemical Modelling with CHASER and WRF/Chem in Japan 181
Masayuki Takigawa, M. Niwano, H. Akimoto, and M. Takahashi
18 Operational Ozone Forecasts for Austria 195
Marcus Hirtl, K. Baumann-Stanzer, and B.C. Kru
¨
ger
19 Impact of Nesting Methods on Model Performance 201

Ursula Bungert and K. Heinke Schlu
¨
nzen
20 Running the SILAM Model Comparatively with ECMWF
and HIRLAM Meteorological Fields: A Case Study in Lapland 207
Marko Kaasik, M. Prank, and M. Sofiev
Part IV Strategy for ACT-NWP Integrated Modeling
21 HIRLAM/HARMONIE-Atmospheric Chemical Transport
Models Integration 215
Alexander Baklanov, Sander Tijm, and Laura Rontu
22 Summary and Recommendations on Integrated Modelling 229
Alexander Baklanov, Georg Grell, Barbara Fay, Sandro Finardi,
Valentin Foltescu, Jacek Kaminski, Mikhail Sofiev,
Ranjeet S. Sokhi, and Yang Zhang
Index 239
Contents ix
.
List of Contributors
Peter Aakjær Danish Meteorological Institute (DMI), Lyngbyvej 100, DK-2100
Copenhagen, Denmark,
Hajime Akimoto Acid Deposition and Oxidant Research Center, 1182 Sowa
Nishi-ku, Nigata-shi 950-2144, Japan,
Bjarne Amstrup Danish Meteorolog ical Institute (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Alexander Baklanov Danish Meteorological Institute (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Max Bangert Institut fu
¨
r Meteorologie und Klimaforschung, Karlsruhe Institute
of Technology (KIT), Postfach 3640, 76021 Karlsruhe, Germany, max.bangert@

kit.edu
Jerzy Bartnicki Norwegian Meteorological Institute (DNMI, met.no), Postboks
43, Blindern 0313, Oslo, Norway, jerzy.ba
Kathrin Baumann-Stanzer Central Institute for Meteorology and Geodynamics,
Hohe Warte 38, 1190 Vienna, Austria,
Dominique Ba
¨
umer Institut fu
¨
r Meteorologie und Klimaforschung, Forschungs-
zentrum, Karlsruhe/Universita
¨
t Karlsruhe, Postfach 3640, 76021, Karlsruhe,
Germany,
Ursula Bungert Meteorological Institute, ZMAW, University of Hamburg,
Bundesstr. 55, 20146 Hamburg, Germany,
Martin E. Cope Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
xi
Alessio D’Allura ARIANET s.r.l, via Gilino 9, 20128, Milano, Italy, a.dallura@
aria-net.it
Antje Dethof European Centre for Medium Range Weather Forecast, Shinfield
Park, RG2 9AX, Reading, UK,
Massimo D’Isidoro Italian National Agency for New Technologies, Energy
and Sustainable Economic Development ENEA, Bologna, Italy; Institute of Atmo-
spheric Sciences and Climate, Italian National Research Council, Rome, Italy,
massimo.disidoro@enea .it
Jerome Fast Pacific Northwest National Laboratory, P.O. 999, MSIN K9-30,
Richland, WA 99352, USA,

Barbara Fay German Weather Service (DWD), Frankfurter Str. 135, 63067,
Offenbach, Germany,
Sandro Finardi ARIANET s.r.l, via Gilino 9, 20128, Milano, Italy, s.finardi@
aria-net.it
Johannes Flem ming European Centre for Medium Range Weather Forecast,
Shinfield Park, RG2 9AX, Reading, UK, joha nnes.fl
Valentine Foltescu Swedish Environmental Protection Agency, 106 48 Stockholm,
Sweden,
Renate Forkel Institute for Meteorology and Climate Research (IMK-IFU), Karls-
ruhe Institute of Technology (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-Parten-
kirchen, Germany,
Saulo Freitas Center for Weather Forecasting and Climate StudiesINPE,
Cachoeira Paulista, Brazil,
Georg Grell National Oceanic and Atmospheric Administration (NOAA)/Earth
System Research Laboratory (ESRL)/Cooperative Institute for Research in Envi-
ronmental Sciences (CIRES), 325 Broadway, Boulder CO 80305-3337, USA,

William I. Gustafson Jr Pacific Northwest National Laboratory, P.O. 999, MSIN
K9-30, Richland, WA 99352, USA,
Helmuth Haak Max Planck Institute for Meteorology, Bundesstrasse 53, 20146,
Hamburg, Germany,
Edwin Haas Institute for Meteorology and Climate Research (IMK- IFU),
Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-
Partenkirchen, Germany,
xii List of Contributors
Marcus Hirtl Central Institute for Meteorology and Geodynamics (ZAMG), Hohe
Warte 38, 1190 Vienna, Austria,
Peter J. Hurley Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,

Marko Kaasik Faculty of Science Technology, Institute of Physics, University
of Tartu, Ta
¨
he 4, 51014 Tartu, Estonia,
Jacek Kaminski Atmospheric Modelling and Data Assimilation Laboratory,
Centre for Research in Earth and Space Science,York University, Toronto, Canada,

Ulrik Korsholm Danish Meteorological Institute (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Bernd C. Kru
¨
ger University of Natural Resources and Applied Life Sciences
(BOKU), Peter-Jordan-Str. 82, 1190 Vienna, Austria,
Sun Hee Lee Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
W. Lilley Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
Ashok K. Luhar Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
Kristina Lundgren Institut fu
¨
r Meteorologie und Klimaforschung, Karlsruhe
Institute of Technology (KIT), Postfach 3640, 76021 Karlsruhe, Germany,

Alexander Mahura Danish Meteorological Institute (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Peter Manins Commonwealth Scientific and Industrial Research Organization

(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
Alberto Maurizi Institute of Atmospheric Sciences and Climate, Itali an National
Research Council, Bologna, Italy,
List of Contributors xiii
John C. McConnell Atmospheric Modelling and Data Assimilation Laboratory,
Centre for Research in Earth and Space ScienceYork University, 4700 Keele Street,
Toronto ON, M3J 1P3, Canada, jcmcc@ yorku.ca
John L. McGregor Commonwealth Scientific and Industrial Research Organiza-
tion (CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VIC,
Australia,
Stuart McKeen National Oceanic and Atmospheric Administration (NOAA)/
Earth System Research Laboratory (ESRL)/Cooperative Institute for Research in
Environmental Sciences (CIRES), 325 Broadway, Boulder CO 80305-3337, USA,

Mihaela Mircea Italian National Agency for New Technologies, Energy and
Sustainable Economic Development ENEA, Bologna, Italy; Institute of Atmospheric
Sciences and Climate, Italian National Research Council, Rome, Italy, mihaela.

Philippe Moinat CNRM-GAME, Me
´
te
´
o-France and CNRS URA, 357, 42 avenue
G. Coriolis, 31057, Toulouse, France,
Lori Neary Atmospheric Modelling and Data Assimilation Laboratory Centre for
Research in Earth and Space Science, York University, 4700 Keele Street, Toronto
ON, M3J 1P3, Canada,
Masaaki Niwano Sumitomo Chemical, 4-2-1 Takatsukasa, Takaraduka Hyogo
665-8555, Japan,

Julie A. Noonan Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia, julie.noonan@ csiro.au
Roman Nuterman Danish Meteorological Institute (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Viel Ødegaard Norwegian Meteorological Institute (DNMI, met.no), Postboks 43,
Blindern, 0313 Oslo, Norway,
Carlos Ordo
´
n
˜
ez Laboratoire d’Ae
´
rologie, 14 avenue Edouard Belin, 31400,
Toulouse, France, carlos.ordonez@metoffice.gov.uk
Steven E. Peckham National Oceanic and Atmospheric Administration (NOAA)/
Earth System Research Laboratory (ESRL)/Cooperative Institute for Research in
Environmental Sciences (CIRES), 325 Broadway, Boulder, CO 80305-3337, USA,

xiv List of Contributors
Vincent-Henri Peuch CNRM-GAME, Me
´
te
´
o-France and CNRS URA, 1357, 42
avenue G. Coriolis, 31057, Toulouse, France,
W.L. Physick Commonwealth Scientific and Industrial Research Organization
(CSIRO), Marine and Atmospheric Research, PMB 1, Aspendale 3195, VI C,
Australia,
Marje Prank Finnish Meteorological Institute, Erik Palmenin aukio 1, P.O.

Box 503, 00101 Helsinki, Finland, marje.pra nk@fmi.fi
Rene
´
Redler NEC Laboratories Europe – IT Division, NEC Europe Ltd., Sankt
Augustin, Germany, rene.red
Rayk Rinke Institut fu
¨
r Meteorologie und Klimaforschung, Karlsruhe Institute of
Technology (KIT), Postfach 3640, 76021 Karlsruhe, Germany,
Lennart Robertson Swedish Meteorological and Hydrological Institute (SMHI),
SE-601 76 Norrko
¨
ping, Sweden,
Laura Rontu Finish Meteorological Institute (FMI), P.O. Box 503, 00101
Helsinki, Finland, laura.rontu@fmi.fi
Marc Salzmann Atmospheric and Oceanic Sciences Program, Princeton Univer-
sity, Princeton, NJ, USA,
K. Heinke Schlu
¨
nzen Meteorological Institute, KlimaCampus, University of
Hamburg, Bundesstr. 55, 20146 Hamburg, Germany,
Martin Schultz ICG-2 Research Center Juelich, Wilhelm-Johnen-Str, 52425
Juelich, Germany,
Arjo Segers TNO, Princetonlaan 6, 3584 CB, Utrecht, The Netherlands, Arjo.

SILAM Team Finnish Meteorological Institute (FMI), P.O. Box 503, 00101
Helsinki, Finland
Mikhail Sofiev Finnish Meteorological Institute (FMI), P.O. Box 503, 00101
Helsinki, Finland, mikhail.sofiev@fmi.fi
Ranjeet S. Sokhi Centre for Atmospheric and Instrumentation Research (CAIR)

University of Hertfordshire College Lane, Hatfield AL10 9AB, UK, r.s.sokhi@
herts.ac.uk
List of Contributors xv
Jens H. Sørensen Danish Meteorological Institut e (DMI), Lyngbyvej 100,
DK-2100 Copenhagen, Denmark,
Tanja Stanelle Institut fu
¨
r Meteorologie und Klimaforschung, Forschungszen-
trum, Karlsruhe/Universita
¨
t Karlsruhe, Postfach 3640, 76021, Karlsruhe, Germany,

Olaf Stein ICG-2 Research Center Juelich, Wilhelm-Johnen-Str, 52425, Juelich,
Germany, o.stein@fz-jue lich.de
Joanna Struzewska Faculty of Environmental EngineeringWarsaw University of
Technology, Nowowiejska 20, 00-653, Warsaw, Poland, joanna.struzewska@is.
pw.edu.pl
Peter Suppan Institute for Meteorology and Climate Research (IMK-IFU),
Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany,

Masaaki Takahashi Center for Climate System Research, University of Tokyo,
5-1-5 Kashiwanoha, Kashiwa 277-8568, Japan,
Masayuki Takigawa Japan Agency for Marine-Earth Science and Technology,
3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan,
takigawa@jamstec. go.jp
Leonor Tarraso
´
n Norwegian Institute for Air research (NILU), Postboks 100,
2027 Kjeller, Norway,
Sander Tijm Royal Netherlands Meteorological Institute (KNM I), Postbus 201,

3730 AE De Bilt, The Netherlands,
Sophie Valcke CERFACS, 42 Av. Coriolis, 31057 Toulouse, France, Sophie.

Heike Vogel Institut fu
¨
r Meteorologie und Klimaforschung, Karlsruhe Institute of
Technology (KIT), Postfach 3640, 76021 Karlsruhe, Germany,
Michiel van Weele Royal Netherlands Meteorological Institute (KNMI), P.O.
Box 201, 3730 AE De Bilt, The Netherlands, wee
Yang Zhang Department of Marine, Earth and Atmospheric Sciences, North
Carolina State University, Raleigh NC 27695, USA,
xvi List of Contributors
.
Chapter 1
Introduction – Integrated Systems: On-line and
Off-line Coupling of Meteorological and Air
Quality Models, Advantages and Disadvantages
Alexander Baklanov
1.1 Introduction
Historically air pollution forecasting and numerical weather prediction (NWP)
were developed separately. This was plausible in the previous decades when
the resolution of NWP models was too poor for meso-scale air pollution fore-
casting. Due to modern NWP models approaching meso- and city-scale resolution
(due to advances in computing power) and the use of land-use databases and remote
sensing data with finer resolution, this situation is changing. As a result the conven-
tional concepts of meso- and urban-scale air pollution forecas ting need revision
along the lines of integration of meso-scale meteorological models (MetMs) and
atmospheric chemical transport models (ACTMs). For example, a new Environ-
ment Canada conception suggests to switch from weather forecasting to environ-
ment forecasting. Some European projects (e.g. FUMAPEX, see: fumapex.dmi.dk)

already work in this direction and have set off on a promising path. In case of
FUMAPEX it is the Urban Air Quality Information and Forecasting Systems
(UAQIFS) integrating NWP models, urban air pollution (UAP) and population
exposure models (Baklanov et al. 2007b), see Fig. 1.1.
In perspective, integrated NWP-ACTM modelling may be a promising way for
future atmospheric simulation systems leading to a new generation of models for
improved meteorological, environmental and “chemical weather” forecasting.
Both, off-line and on-line coupling of MetMs and ACTMs are useful in different
applications. Thus, a time ly and innovative field of activity will be to assess their
interfaces, and to establish a basis for their harmonization and benchmarking. It will
consider methods for the aggregation of epis odic results, model down-scaling as
well as nesting. The activity will also address the requirements of meso-scale
meteorological models suitable as input to air pollution models.
The COST728 Action () addressed key issues con-
cerning the development of meso-scale modelling capabilities for air pollution
A. Baklanov
Danish Meteorological Institute (DMI), Lyngbyvej 100, DK-2100 Copenhagen, Denmark
e-mail:
A. Baklanov et al. (eds.), Integrated Systems of Meso-Meteorological
and Chemical Transport Models, DOI 10.1007/978-3-642-13980-2_1,
#
Springer-Verlag Berlin Heidelberg 2011
1
and dispersion applications and, in particular, it encouraged the advancement of
science in terms of integration methodologies and strategies in Europe. The final
integration strategy will not be focused around any particular model; instead it will be
possible to consider an open integrated system with fixed architecture (module
interface structure) and with a possibility of incorporating different MetMs/NWP
models and ACTMs. Such a strategy may only be realised through jointly agreed
specifications of module structure for easy-to-use interfacing and integration.

The overall aim of working group 2 (WG2) of the COST 728 Action, “Integrated
systems of MetM and ACTM: strategy, interfaces and module unification”, is to
identify the requirements for the unification of MetM and ACTM modu les and to
propose recommendations for a European strategy for integrated mes o-scale mod-
elling capabilities. The first report of WG2 (Baklanov et al. 2007a) compiles
existing state-of-the -art methodolo gies, approaches, models and practices for
building integrated (off-line and on-line) meso-scale systems in differen t, mainly
European, countries. Th e report also inc ludes an overview and a summary of
existing int egrated models and their characteristics as they are presently used.
The model contributions were compiled using COST member contributions, each
focusing on national model systems.
FUMAPEX UAQIFS:
Meteorological models for urban areas
Interface to Urban Air Pollution models
Urban heat
flux param-
eterisation
Soil & sub-
layer models
for urban areas
Mixing height
and eddy
diffusivity
estimation
Populations/
Groups
Down- scaled
models or ABL
parameterisations
Estimation of

additional advanced
meteorological
paramaters for UAP
Urban roughness
classification &
parameterisation
Use of satellite
information
on surface
Meso- / City - scale NWP models
Urban Air Pollution models
Population Exposure models
Grid adaptation
and interpolation,
assimilatiom of
NWP data
Micro
environments
Outdoor concentrations
Indoor concentrations
Time activity
Exposure
Module of
feedback
mechamisms:
- Direct gas &
aerosol forcing
- Cloud condensa-
tion nuclei model
- Other semidirect

& indirect effects
All 3D
meteorological
& surface
fields are
available at
each time step
Fig. 1.1 Extended FUMAPEX scheme of Urban Air Quality Information & Forecasting System
(UAQIFS) including feedbacks. Improvements of meteorological forecasts (NWP) in urban areas,
interfaces and integration with UAP and population exposure models following the off-line or on-
line integration (Baklanov 2005; after EMS-FUMAPEX 2005)
2 A. Baklanov
1.2 Methodology for Model Integration
The modern strategy for integrating MetMs and ACTMs is suggested to incorporate
air quality modelling as a combination of (at least) the following factors: air
pollution, regional/urban climate/meteorological conditions and population expo-
sure. This combination is reasonable due to the following facts: meteorology is the
main source of uncertainty in air pollution and emergency preparedness models,
meteorological and pollution components have complex and combined effects on
human health (e.g., hot spot s in Paris, July 2003), pollutants, especially aerosols,
influence climate forcing and meteorological events (such as, precipitation and
thunderstorms).
In this context, several levels of MetM and ACTM coupling/integration can be
considered:
Off-Line
l
Separate ACTMs driven by meteorological input data from meteo-preproces-
sors, measurements or diagnostic models
l
Separate ACTMs driven by analysed or forecasted meteodata from NWP

archives or datasets
l
Separate ACTMs reading output-files from opera tional NWP models or specific
MetMs at limited time intervals (e.g. 1, 3, 6 h)
On-Line
l
On-line access models, when meteod ata are available at each time-step (possibly
via a model interface as well)
l
On-line integration of ACTM into MetM, where feedbacks may be considered.
We will use this definition for on-line coupled/integrated modelling
The main advantages of the On-line coupled modelling approach comprise:
l
Only one grid is employed and no interpolation in space is required
l
There is no time interpolation
l
Physical parametrizations and numerical schemes (e.g. for advection) are the
same; No inconsistencies
l
All 3D meteorological variables are available at the right time (each time
step)
l
There is no restriction in variability of meteorological fields
l
Possibility exists to consider feedback mechanisms, e.g. aerosol forcing
l
There is no need for meteo- pre/post-processors.
However, the on-line approach is not always the best way of the model integra-
tion. For some specific tasks (e.g., for emergency preparedness, when NWP data are

available) the off-line coupling is more efficient way.
1 Introduction – Integrated Systems: On-line and Off-line Coupling of Meteorological 3
The main advantages of Off-line mode ls comprise:
l
There is the possibility of independent parametrizations
l
They are more suitable for ensembles activities
l
They are easier to use for the inverse modelling and adjoint problem
l
There is the independence of atmospheric pollution model runs on meteorologi-
cal model computations
l
There is more flexible grid construction and generation for ACTMs
l
This approach is suitable for emission scenarios analysis and air quality man-
agement.
The on-line integration of meso-scale meteorological models and atmospheric
aerosol and chemical transport models enables the utilisation of all meteorological
3D fields in ACTMs at each time step and the consideration of two-way feedbacks
between air pollution (e.g. urban aerosols), meteorological processes and climate
forcing. These integration methodologies have been demonstrated by several of the
COST action partners such as the Danish Meteorological Institute, with the DMI-
ENVIRO-HIRLAM model (Chenevez et al. 2004; Baklanov et al. 2004, 2008;
Korsholm et al. 2007) and the COSMO consortium with the Lokal Modell (Vogel
et al. 2006; Wolke et al. 2003).
These model developments will lead to a new generation of integrated models
for: climate change modelling, weather forecasting (e.g., in urban areas, severe
weather events, etc.), air quality, long-term assessments of chemical composition
and chemical weather forecas ting (an activity of increasing importance which is

supported by a new COST action ES0602 started in 2007).
1.3 Overview of European On-Line Integrated Models
The experience from other European, as well as non-European union communities,
will need to be integrated. On our knowledge on-line coupl ing was first employed at
the Novosibirsk scientific school (Marchuk 1982; Penenko and Aloyan 1985;
Baklanov 1988), for modelling active artificial/anthropogenic impacts on atmo-
spheric processes. Currently American, Canadian and Japanese institutions develop
and use on-line coupled models operationally for air quality forecasting and for
research (GATOR-MMTD: Jacobson, 2005, 2006; WRF-Chem: Grell et al. 2005 ;
GEM-AQ: Kaminski et al. 2005).
Such activities in Europe are widely dispersed and a COST Action seems to
be the best approach to integrate, streamline and harmonize these national efforts
towards a leap forward for new breakthroughs beneficial for a wide community of
scientists and users.
Such a model integration should be realized fol lowing a joint elaborated specifi-
cation of module structure for potential easy interfacing and integration. It might
4 A. Baklanov
develop into a system, e.g. similar to the USA ESMF (Earth System Modelling
Framework, see e.g.: Dickenson et al. 2002) or European PRISM (PRogram for
Integrating Earth System Modelling) specification for integrated Earth System
Models: (Valcke et al. 2006).
Community Earth System Models (COSMOS) is a major international project
() involving different institutes in Europe, in the US and in
Japan, for the development of complex Earth System Models (ESM). Such mode ls
are needed to understand large climate variations of the past and to predict future
climate changes.
The main differences between the COST-728 integrating strategy for meso-scale
models and the COSMOS integration strategy regards the spatial and temporal
scales. COSMOS is focusing on climate time-scale processes, general (global
and regional) atmospheric circulation models and atmosphere, ocean, cryosphere

and biosphere integration, while the meso-scale integr ation strategy will focus on
forecast time-scales of 1 to 4 days and omit the cryosphere and the larg er temporal
and spatial scales in atmosphere, ocean and biosphere.
The COST728 model overview (Baklanov et al. 2007a) shows a surprisingly
large (at least ten) numb er of on-line coupled MetM and ACTM systems already
being used in Europe (see also more information in Table 1.1):
l
BOLCHEM (CNR ISAC, Italy)
l
ENVIRO-HIRLAM (DMI, Denmark)
l
LM-ART (Inst. for Meteorology and Climate Research (IMK-TRO), KIT,
Germany)
l
LM-MUSCAT (IfT Leipzig, Germany)
l
MCCM (Inst. for Meteorology and Climate Research (IMK-IFU), KIT, Germany)
l
MESSy: ECHAM5 (MPI-C Mainz, Germany)
l
MC2-AQ (York Univ, Toronto, University of British Columbia, Canada, and
Warsaw University of Technology, Poland)
l
GEM/LAM-AQ (York Univ, To ronto, University of British Columbia, Canada,
and Warsaw University of Technology, Poland)
l
WRF-CHem: Weather Research and Forecast and Chemistry Community
modelling system (NCAR and man y other organisations)
l
MESSy: ECHAM5-Lokalmodell LM planned at MPI-C Mainz, Univ. of Bonn,

Germany
However, it is necessary to mention, that many of the above on-line models were
not build for the meso-meteoro logical scale, and several of them (GME, MESSy)
are global-scale modelling systems, originating from the climate modelling com-
munity. Besides, at the current stage most of the on-line coupled models do not
consider feedback mechanisms or include only simple direct effects of aerosols on
meteorological processes (COSMO LM-ART and MCCM). Only two meso-s cale
on-line integr ated modelling systems (WRF-Chem and ENVIRO-HIRLAM) con-
sider feedbacks with indirect effects of aerosols.
1 Introduction – Integrated Systems: On-line and Off-line Coupling of Meteorological 5
1.4 Feedback Mechanisms, Aerosol Forcing in
Meso-meteorological Models
In a general sense air quality and ACTM modelling is a natural part of the climate
change and MetM/NWP modelling process. The role of greenhouse gases (such as
water vapour, CO
2
,O
3
and CH
4
) and aerosols in climate change has been high-
lighted as a key area of future research (Watson et al. 1997; IPCC 2007, 2001;
AIRES 2001). Uncertainties in emission projections of gaseous pollutants and
aerosols (especially secondary organic components) need to be addressed urgently
to advance our understanding of climate forcing (Semazzi 2003). In relat ion to
aerosols, their diverse sources, complex physicochemical characteristics and large
spatial gradients make their role in climate forcing particularly challenging to
Table 1.1 On-line coupled MetM – ACTMs (Baklanov et al. 2007a)
Model name On-line coupled chemistry Time step for coupling Feedback
BOLCHEM Ozone as prognostic chemically

active tracer
None
ENVIRO-HIRLAM Gas phase, aerosol and
heterogeneous chemistry
Each HIRLAM time
step
Yes
WRF-Chem RADM+Carbon Bond,
Madronich+Fast-J photolysis,
modal+sectional aerosol
Each model time step Yes
COSMO LM-ART Gas phase chem (58 variables),
aerosol physics (102
variables), pollen grains
Each LM time step Yes
a
COSMO LM-
MUSCAT
b
Several gas phase mechanisms,
aerosol physics
Each time step or time
step multiple
None
MCCM RADM and RACM, photolysis
(Madronich), modal aerosol
Each model time step (Yes)
a
MESSy: ECHAM5 Gases and aerosols Yes
MESSy: ECHAM5-

COSMO LM
(planned)
Gases and aerosols Yes
MC2-AQ Gas phase: 47 species, 98
chemical reactions and 16
photolysis reactions
Each model time step None
GEM/LAM-AQ Gas phase, aerosol and
heterogeneous chemistry
Set up by user – in most
cases every time step
None
Operational ECMWF
model (IFS)
Prog. stratos passive O3 tracer Each model time step Yes
ECMWF GEMS
modelling
GEMS chemistry
GME Progn. stratos passive O3 tracer Each model time step
OPANA¼MEMO
+CBMIV
Each model time step
a
Direct effects only
b
On-line access model
6 A. Baklanov
quantify. In addition to primary emissions, secondary particles, such as, nitrates,
sulphates and organic compounds, also result from chemical reactions involving
precursor gases such as SO

x
, DMS, NO
x
, volatile organic compounds and oxidising
agents including ozone. One consequence of the diverse nature of aerosols is
that they exhibit negative (e.g. sulphates) as well as positive (e.g. black carbon)
radiative forcing characteristics (IPCC 2007, 2001; Jacobson 2002). Although
much effort has been directed towards gaseous species, considerable uncertainties
remain in size dependent aerosol compositional data, physical properties as well as
processes controlling their transport and transformation, all of which affect the
composition of the atmosphere (Penner et al. 1998; Shine 2000; IPCC 2007, 2001).
Probably one of the most important sources of uncertainty relates to the indirect
effect of aerosols as they also contribute to multiphase and microphysical cloud
processes, which are of considerable importance to the global radiative balance
(Semazzi 2003).
In addition to better parameterisations of key processes, improvements are
required in regional and global scale atmospheric modelling (IPCC 2005;
Semazzi 2003). Resolution of regional climate information from atmo-
sphere-ocean general circulation models remains a limiting factor. Vertical
profiles of temperature, for example, in climate and air quality models need
to be better described. Such limitations hinder the prospect of reliably
distinguishing between natural variability (e.g. due to natural f orcing agents,
solar irradiance and volcanic effects) and human induced changes caused by
emissions of greenhouse gases and aerosols over multidecadal timescales
(Semazzi 2003). Consequently, the current predictions of the impact of air
pollutants on climate, air quality and ecosystems or of extreme events are
unreliable (e.g. Watson et al. 1997). Therefore it is very important in the
future research to address all the key areas of uncertainties so as provide an
improved modelling capability over regional and global scales and an
improved integrated assessment methodology for formulating mitigation and

adaptation strategies.
In this concern one of the important tasks is to devel op a modelling instrument of
coupled “Atmospheric chemistry/Aerosol” and “Atmospheric Dynamics/Climate”
models for integrated studies, which is able to consider the feedback mechanisms,
e.g. aerosol forcing (direct and indirect) on the meteorological processes and
climate change (see Fig. 1.2).
Chemical species influencing weather and atmospheric processes include green-
house gases which warm near-surface air and aerosols such as sea salt, dust,
primary and secondary particles of anthropogenic and natural origin. Some aerosol
particle components (black carbon, iron, aluminium, polycyclic and nitrated aro-
matic compounds) warm the air by absorbing solar and thermal-IR radiation, while
others (water, sulphate, nitrate, most of organic compounds) cool the air by
backscattering incident short-wave radiation to space.
It is necessary to highlight those effects of aerosols and other chemical species
on meteorological parameters have many different pathways (such as, direct,
indirect and semi-direct effects) and they have to be prioritized and considered in
1 Introduction – Integrated Systems: On-line and Off-line Coupling of Meteorological 7